This project will develop an innovative system for significantly reducing the levels of empty and part-filled running, ie backloading, of freight road vehicles. This will lead to reduced freight operating costs, fuel usage and carbon emissions. It will achieve these aims by developing processes that overcome existing issues with 'embedded behaviours' and enable improved matching of the 'available empty and part-filled load journeys' of freight enterprises with customer's demands for goods to be moved. Current vehicle backloading planning and routing systems are only fully capable of supporting one-way outbound distribution. The result is that average freight vehicle loading utilisation factors are less then 40% and empty running of vehicles accounts for ~29% of total UK freight vehicle kilometres. Some capability for using software solutions to improve efficiency by integrating demand for consignment movement to reduce vehicle-miles with empty or minimal vehicle loads exists, however human intervention is required due to the short-term and highly-variable demand involved in the movement of goods. This project will build on DMU's existing expertise with knowledge management and AI planning and result in significant beneficial effects on transport networks by reducing the ~8 billion miles currently travelled by empty and part-filled freight vehicles and the ~1 billion kgCO2e emitted by freight vehicles during this travel. It will support the competitiveness of the UK logistics industry by producing a marketable solution providing realised systems capable of integration with existing distribution planning software that operators see value in and will want to use and buy.